As the ventricular fibrillation morbidity constantly increase over the past ten years or so, while no remarkable improvement has been made in saving lives from the disease, the issues of automatic detection of ventricular fibrillation and defibrillation thereof have drawn increasing attention from all the society. As early as the beginning of 1990s, such cardiology authority as the “American Heart Association” (AHA) appealed for urgent efforts on developing automatic defibrillation facilities.
In view of the detection methods developed in recent years, it can be learned that commonly used at present are a time-field detection method, a frequency-field detection method and a time-frequency analysis detection method, as well as relevant dynamics analysis method and so on.
In particular, the patent document WO0224276, tilted “System and Method for Complexity Analysis-Based Cardiac Tachyarrhythmia Detection” discloses a complexity-based method for detecting ventricular fibrillation as follow.
1. Pass signal through a filter in the range of 3 to 33 Hz;
2. Calculate a heart rate (HR) indicated by the signal;
3. Convert the data into 0 and 1, which comprises:                (1) obtaining the mean of all the positive R-waves (MPRV) as well as the mean of all the negative R-waves (MNRV) respectively; wherein if no negative R-wave exists, setting MNRV to be 33% of the MPRV (MNRV=33% MPRV), while if no positive R-wave exists, then MPRV to 33% of the MNRV (MPRV=MNRV×33%);        (2) obtaining the number of samples whose values fall between 10% of MNRV and 10% of MPRV, i.e. Baselinedata; then determining the ratio based on “ratio=Baselinedata/N”, wherein N represents the total number of samples;        (3) if the ratio is smaller than or equal to 20%, setting a threshold Td as 0 (Td=0); if the ratio is larger than 20%, comparing the number of positive R-wave (NPR) to the number of negative R-wave (NNR), wherein if NPR is smaller than NNR, then Td is set to MPRV/2 (Td=MPRV/2); otherwise, Td is set to MNRV/2 (Td=MNRV/2).        (4) converting the amplitude value sequence A1, A2, . . . AN of the sampled signal into 0 and 1 base on Td, wherein if Ai is larger than Td, Ai is set as 1; otherwise, it is set as 0 if Ai<=Td.        
4. Calculate the complexity of the converted sequence using Lempel-Ziv algorithm:                (1) Defining a binary character sequence formed of the converted A1, A2, . . . AN as S1, S2, . . . Sn, wherein S=S1S2 . . . Sr, and Q=Sr+1, 0≦r≦n−1.        (2) Designating SQ to represent a general character string formed of two concatenated strings S, Q, and SQV to represent a character string obtained after with the last character of SQ deleted, i.e., SQV=S1S2 . . . Sr. It is determined whether Q is contained in SQV. If Q is not contained in SQV, then Sr+1 is added to S. In that case, the complexity C(n) increments, and the process proceed to determine the next character Sr+2. If Q=Sr+1 is contained in SQV, it is then determined whether Q=Sr+1Sr+2 is contained in SQV, here SQV=S1S2 . . . SrSr+1. If so, then Q=Sr+1Sr+2Sr+3 is further determined.        (3) Continuing like this, the procedure may result in two possibilities: either that Q contains the last symbol Sn of the originally given sequence so that the analysis is closed, or that if any Q=Sr+1Sr+2 . . . Sr+i is not contained in SQV, then the Q is added to S, S=S1S2 . . . SrSr+1 . . . Sr+i, and the complexity C(n) increments. Thereby, a final result of the complexity C(n) can be calculated. Take the sequence 001111000011100001111001100011110001 for example. According to the Lempel-Ziv algorithm, Q character strings thereof are defined as 0.01.1110.0001.1100001111.00110.00111100.01 after each determination, with each Q string shown in between every two punctuation. Each of the punctuation defines that the Q string before the punctuation is not a sub-string of the entire long string minus the last character made up by all characters before the punctuation. Thereby, the complexity is calculated as 8.        
5. Set a heart-rate threshold TDR and complexity thresholds LCT, MCT, and HCT, and directly determine a ventricular fibrillation or a ventricular tachycardia based on the calculated heart rate and complexity, as shown in the flow chart of FIG. 1.
In the prior art as described above, there are the following disadvantages:
(1) The calculation according to the given algorithm is complicated This is mainly reflected by the complicated calculation of heart rate and lots of times the character string are compared during the calculation of complexity.
(2) This detection method directly determines a ventricular fibrillation through analysis of heart rate and complexity. In practice, however, it may easily lead to wrong judgment since ventricular tachycardia, superventricular tachycardia, AF and AFL all may involve high complexity.
(3) Sensitivity and specificity are both low. That is mainly because it does not accurately differentiate the cases of ventricular fibrillation (VF), ventricular tachycardia (VT), atrial fibrillation (AF), atrial flutter (AFL) and superventricular tachycardia (SVT) from each other, and also fails to take into consideration the influence of various noises. As a result, it falls behind the clinical needs.